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Application of artificial neural networks to the prediction of antifungal activity of imidazole derivatives against Candida albicans
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NSTL
Elsevier
The article uses artificial neural networks (ANN) to predict the antifungal properties of quaternary ammonium salts against Candida albicans. The antifungal activity expressed as the minimum inhibitory concentration (MIC) of microbial growth was determined experimentally by serial dilution method for a series of 140 new imidazole derivatives. Then, three-dimensional models of test compounds were constructed and the chemical information was converted into a useful number using computational chemistry. In the next step, neural network models were designed to solve regression and classification problems. Both models were characterized by high predictive ability. The quality of the regression model was determined on the basis of the level of correlation between the theoretically calculated activity and the activity determined experimentally (R-2 = 0.91 for the learning set, R-2 = 0.88 for the test set and R-2 = 0.91 for validation). The classification model differentiated the compounds into active or inactive with a classification accuracy of 91.67% for the learning set, 88.57% for the test set, and 95.24% for the validation set. Artificial neural networks are a predictive tool with impressive learning properties and non -linear information processing capabilities. ANN have the potential to reduce time and costs of discovering new antimicrobial substances and to support pharmaceutical development research.
NSTL
Elsevier
